Protein supplementation changes gut microbial diversity and derived metabolites in subjects with type 2 diabetes

Summary High-protein diets are promoted for individuals with type 2 diabetes (T2D). However, effects of dietary protein interventions on (gut-derived) metabolites in T2D remains understudied. We therefore performed a multi-center, randomized-controlled, isocaloric protein intervention with 151 participants following either 12-week high-protein (HP; 30Energy %, N = 78) vs. low-protein (LP; 10 Energy%, N = 73) diet. Primary objectives were dietary effects on glycemic control which were determined via glycemic excursions, continuous glucose monitors and HbA1c. Secondary objectives were impact of diet on gut microbiota composition and -derived metabolites which were determined by shotgun-metagenomics and mass spectrometry. Analyses were performed using delta changes adjusting for center, baseline, and kidney function when appropriate. This study found that a short-term 12-week isocaloric protein modulation does not affect glycemic parameters or weight in metformin-treated T2D. However, the HP diet slightly worsened kidney function, increased alpha-diversity, and production of potentially harmful microbiota-dependent metabolites, which may affect host metabolism upon prolonged exposure.


INTRODUCTION
Cardio-metabolic diseases (CMD) represent an umbrella term encompassing, among other, type 2 diabetes (T2D) and cardiovascular diseases. 1 CMD are currently on the rise globally and are the leading causes of morbidity and mortality. [2][3][4] The pathophysiology of CMD is complex, but one important factor recognized to be involved is insulin resistance, which is a hallmark trait in the onset of T2D and glycemic control in individuals with T2D. 5 A key element in management of patients with T2D is to provide diet guidance as multiple dietary interventions have shown the potential of diet manipulation to positively affect metabolic health. 6,7 We therefore performed a 12-week randomized, multi-center, isocaloric dietary intervention comparing high protein (HP; protein: 30% of energy intak[ (En%]) vs. low protein (LP; protein: 10% En%) intake in a multi-ethnic population of individuals with T2D on stable metformin. Our primary objective was to investigate the effects of this isocaloric protein modulation on glycemic control and insulin resistance markers in individuals with T2D. Secondary objectives were to analyze the impact of an increase in protein consumption on gut microbiota composition and functional metabolic output as monitored by plasma levels of microbiota-derived metabolites.

Baseline characteristics
A total of 171 subjects were randomized to either the HP or LP group. The study design is displayed in Figure 1. Out of these subjects, 151 (78 HP and 73 LP diet) finalized the clinical trial and were included in the analyses. Most common reasons for dropout were antibiotic usage during the trial (N = 3), failure to adhere to the diet (N = 6), or personal problems (N = 6) ( Figure 2 for CONSORT flow chart). Baseline characteristics are shown in Table 1. At baseline there were no statistically significant differences in biochemical or anthropometric parameters. Subjects had a mean age of 58.2 G 7.7 years in the HP group and 59.1.1 G 7.0 years in the LP group. The groups mainly consisted of women, with 56.4% in the HP group and 58.9% in the LP group. All subjects were on stable metformin therapy and a subset also used other diabetes lowering drugs, such as sulfonylureas. However, these subjects were equally distributed among the HP and LP groups. Of note, there were also no statistically significant differences at baseline when subjects were stratified according to center (Table S1).
A 12-week isocaloric high vs. low protein dietary intervention does not affect BMI, body composition or biochemical parameters with the exception of renal function As previous studies have reported both beneficial (increased satiety and weight loss), as well as detrimental (renal damage) effects of a HP diet, we next determined the effects of an HP vs. LP diet on body weight, body composition, and biochemical parameters ( Figure 3E; Table 3). This study did not find significant weight changes or body composition changes following the isocaloric intervention. Moreover, lipid levels, inflammation markers, and energy expenditure were not affected. However, estimated renal function decreased in the HP group by 1.67 G 15.3 mL/min/1.73m2 and improved in the LP group 3.0 G 12.98 mL/min/1.73m2 (p = 0.03).

Figure 1. Study design MICRODIET trial
Subjects were randomized to follow either a high protein (HP) or low protein (LP) diet for 12 weeks. Study visits were performed at week 0 (baseline), week 6, and week 12 (end of intervention). A mixed-meal test (MMT) was performed at week 0 and week 12 and plasma for metabolomics was also obtained. Dietary adherence was observed through weekly contact with a dietician and the use of weekly food diaries. Before each study visit subjects collected 24-h urine and as well as 24-h fresh feces. We next determined the effects of the dietary intervention on gut microbiota composition and diversity using metagenomic approaches ( Figure 5A). This 12-week HP vs. LP intervention did elicit changes in the gut microbiota composition albeit with a modest effect; the explained variance of the beta-diversity was 0.146% (p < 0.001) ( Figure S4). Moreover, alpha diversity, increased in the HP group by 2.6% and decreased in the LP group by 0.2% (HP vs. LP p = 0.01) ( Figure 5A). Interestingly, the effects of the protein intervention on gut microbiota composition were not driven by large changes in individual taxa ( Figure S5). Moreover, the respective diets did not induce significant functional changes in the gut microbiota ( Figure S6).
As gut microbiota composition is related to microbiota-derived metabolite production, we next measured if the protein interventions elicited changes in the measured plasma metabolite profile ( Figure 5B). We therefore determined fasting and postprandial (t = 240 min during MMT) plasma levels of several (proteinderived) metabolites. Interestingly, a majority of the observed metabolite changes were driven by increases of the metabolite levels in the HP group. The main metabolites that increased in the HP group were PAG (log fold change: 0.32 i.e., 38% increase); indoxyl sulfate (log fold change: 0.21 i.e., 23% increase), indole-3-acetic acid (log fold change: 0.15 i.e., 16% increase), homocitruline (log fold change: 0.15 i.e., 16% increase), and propionyl carnitine acid (log fold change: 0.13 i.e., 14% increase) in the fasting metabolites. In the postprandial samples phenylacetic acid, PAG, indoxyl sulfate, homocitruline, and propionyl carnitine were significantly affected by the dietary intervention. In the LP group, changes were more limited but we found an increase in indole 3 propionic acid (log fold change: 0.43 i.e., 44% increase). All previously described shifts in metabolites were statistically significant after correction for baseline value, center, and estimated glomerular filtration rate (eGFR). No changes on ImP levels were induced by the protein modulation. To determine the relationship between taxa-and plasma metabolite levels, we tested associations with linear mixed effect models and found several relationships between individual taxa and an increase in the associated metabolite ( Figure 6). The results show cross-sectional associations displaying the relationship between individual taxa and associated serum metabolites. However, when taking the dietary intervention into account, only four taxa-metabolite relationships remained significant, and most associations were therefore driven by the dietary intervention. The HP diet increased the Dorea sp.CAG:105, which was correlated with an increase in p-cresol sulfate; Firmicutes bacterium CAG:102 and Oscilibacter sp (both correlated with an increase in PAGln) and Roseburia sp (which correlated with an increase in Indoxyl sulfate). The LP diet was associated with decreased levels of aforementioned species and metabolites, indicating again a diet-driven response ( Figure S7).

DISCUSSION
In this 12-week randomized HP vs. LP dietary intervention without caloric restriction in individuals with T2D treated with metformin, we did not observe any significant effects of protein modulation on post prandial glycemic excursions, HbA1c, and other metabolic parameters. However, protein supplementation induced a small increase in microbial diversity and significant changes in gut microbial-derived metabolomic profiles.
We found no effect of a short-term HP vs. LP dietary intervention on glycemic markers despite detailed phenotyping using post-prandial glycemic excursion after a MMT, HbA1c, HOMA-IR, and real-world data with continuous glucose monitoring. Importantly, long-term observational studies have shown an association between increased animal protein intake and T2D incidence, whereas plant protein consumption had a neutral effect. [12][13][14][15] These results are not contradicted by our study performed in individuals with installed T2D but should not be extrapolated to individuals without T2D for whom protein intake has been iScience Article associated with increased insulin secretion and hepatic production of glucose, which ultimately may lead to increased insulin resistance and T2D development. 46 On the other hand, in individuals with T2D, low-calorie HP dietary interventions reported improved metabolic markers 16,17 contrary to our results. However, in these studies, the effects of HP diet may have been confounded by the caloric-restriction induced weight loss, which was more important in HP diets and is a confounding factor when studying metabolic parameters. Here, we carefully controlled this potential confounder by providing isocaloric diet in both HP and LP groups. Consequently, the main change was the modulation of macronutrients as shown by the little observed weight loss (approximately 1 kg), which was not different between the two groups. Therefore, this study allows to interpret solely the effect of protein modulation without the bias of differences in weight loss between the groups. (B) Self-reported macronutrient consumption at baseline and end of the study period. Significant differences in protein energy percentage (En%) and carbohydrate intake between baseline and end of intervention period in HP and LP group. The effect of the intervention (HP vs. LP) on changes from baseline (delta between week 12 and week 0) was analyzed in a linear regression model adjusting for baseline values and center. (C) Fiber intake between HP and LP group throughout the intervention (ns).
(D) 24-h urine urea/creatinine ratios. In the HP group statistical significant increase between week 0 and week 6 and week 0 and week 1, no statistical significance between week 6-week 12. In the LP group statistical significant decrease between week 0 and week 6 and week 0 and week 12, no statistical significance between week 6-week 12. Data were analyzed using a linear-mixed effects model with post-hoc Dunn's correction.
(E) BMI (body-mass index) between HP and LP group throughout the intervention (ns). All data are presented as mean G SD. A p < 0.05 was considered statistically significant (indicated with an *). iScience Article Renal function was significantly changed by the protein intervention with a decrease in the HP group opposed to an improvement in the LP group. The effect was rather small with little clinical relevance, but this result questions what may be the outcomes of longer-term HP diets on renal function. Previous meta-analysis showed that indeed, an LP diet may improve renal function in T2D, without effecting glycemic parameters. 47 Whether an HP diet significantly and clinically worsens renal functions remains a topic of discussion 48 In this controlled setting, the HP vs. LP intervention induced an increase in gut microbiota diversity, due to the HP group. Previous studies in individuals without T2D have reported that a HP, caloric-restricted dietary intervention was associated with an increase in microbiota diversity sometimes reaching 30% increase. 49,50 Our result suggests that HP diet per se (e.g., without calorie restriction) could be associated with increased alpha diversity albeit with a low magnitude. Indeed, the observed increase in diversity is moderate (2.4% increase in Shannon index) and probably not sufficient to restore microbiota richness in subjects with severe dysbiosis which has been linked with metabolic diseases. 51 This suggests that to significantly and more dramatically increase microbial diversity, combining caloric restriction with macronutrient changes might be more efficient.
Furthermore, this study found that protein modulation did result in changes in plasma metabolite profile, mainly driven by the HP group. Of note, these changes were seen without large effects on gut microbiota composition or function as evaluated via metagenomics analysis. One possible explanation for this, is the potential of diet to induce post-translational changes, 52 i.e., changes that can be more pronounced in the transcriptome which is undetectable via metagenomics.
The main metabolites monitored that increased due to the HP diet were PAG and indoxyl sulfate, both of which have been clinically and mechanistically associated with cardiovascular risk. 39,53,54 PAG is a gutderived metabolite produced from the essential amino acid phenylalanine. Increased PAG levels have been associated with cardiovascular disease and increased thrombosis potential. 39 Indoxyl sulfate is a gut-derived uremic toxin which has been linked to chronic kidney disease and cardiovascular disease, possibly via increased inflammation, endothelial dysfunction, and higher levels of cardiac fibrosis. 53 In the LP group the main metabolite change was a significant increase in indole-3-propionic acid. This gut microbiota generated metabolite of the essential amino acid tryptophan may be a protective factor against atherosclerosis by promoting reverse cholesterol transport and is down-regulated in patients with atherosclerotic cardiovascular disease. 55 Moreover, indole-3-propionic acids has been clinically and mechanistically been linked to diabetes and other metabolic disorders. 56 The protein content of the diet had a neutral impact on ImP, a metabolite of the essential amino acid histidine. Although this result may be surprising, it confirms the absence of link between histidine consumption and ImP levels previously reported in an observational study from the European MetaCardis population. 57 iScience Article This further supports the hypothesis that dietary protein may be metabolized in the small intestine but that other macronutrients, e.g., dietary fiber, can modulate the gut microbiota structure and function thus affecting enzyme activities and metabolite production where substrates originate from host or microbial proteins. 40 On the other hand, we did not observe significant metagenomic functional changes with the intervention whereas metabolites change significantly with a relatively important effect size. This suggests that for these metabolites the most modification of the gut environment with the protein modulation was sufficient to change their production without changes in abundance of metagenomic functions. It is also possible that duration of the intervention was not sufficient to induce significance metagenomic functional shifts and that a longer intervention would have resulted in both metagenomic functional changes and metabolites changes.
Since diet induced small, but significant, changes in gut microbiota diversity and also led to a change 58 in plasma metabolite profile, we next determined associations between microbial taxa and plasma metabolites. This study identified several associations between taxa and metabolites, such as Oscillibacter and indoxyl sulfate levels, which has been previously reported. 58 However, when specifying to the intervention in this study, only four associations remained. The HP diet increased the Dorea sp.CAG:105, which was correlated with an increase in p. cresolsulfate; Firmicutes bacterium CAG:102, Oscilibacter sp (both correlated with an increase in PAGln), and Roseburia sp (which correlated with an increase in Indoxyl sulfate). The LP diet decreased aforementioned species and metabolites. More research is warranted in these specific taxa-metabolite relationships, as these can serve as potential targets of specific dietary interventions.
In conclusion, this multi-center, 12-week, randomized-controlled isocaloric, dietary protein intervention in individuals with T2D subjects on metformin treatment showed that a short-term protein modulation does not iScience Article affect glycemic parameters, but an HP diet results in a small increase in serum creatinine. Moreover, the HP diet vs. the LP diet leads to changes in gut microbiota composition and production of (gut-derived) metabolites which are themselves known to be associated with cardiovascular risk. Furthermore, this study identified several taxa-metabolite associations that were diet driven. More research is needed with longer duration and study population in order to investigate and validate potential causal effects of these findings in CMD.

Limitations of the study
This study has limitations and strengths. A first limitation is the relatively short duration of the study and exposition to the dietary modification. Indeed, it is possible that with a more prolonged exposure, the dietary intervention may have had a significant effect on metabolic outcomes. Another limitation is that our sample size may have not been large enough to detect smaller metabolic changes. However, our power analysis did show sufficient power for the primary outcome. Moreover, the population studied was heterogeneous and used several antidiabetic drugs, which could have influenced gut microbiota composition and function as shown in previous metagenomic analysis. 59 Although half of the diet was directly provided to the patients, we did not control all of the dietary and beverage intake of the participants during the study, as this was not feasible for such a relatively large group. Another limitation is the fact that, by design, protein was not the only macronutrient modified by the dietary intervention study, as carbohydrates were also significantly changed in order maintain an isocaloric diet. However, we can note that the main difference in terms of dietary intake between the 2 groups is the protein content which is double the amount in HP vs. LP when the difference in carbohydrates or fat intake between the two groups is much less pronounced. Moreover, fiber intake was kept constant throughout the study to avoid confounding effects. Moreover, this study only investigated post-prandial metabolite changes after 240 min at baseline and after a 12 weeks intervention. However, this study did identify several metabolites that had altered post-prandial levels after 12 weeks of protein intervention compared to baseline. However, studies are needed investigating longer post-prandial timepoints to ensure that these changes are not transient, but can, with prolonged duration of intervention, affect host metabolism.
Strengths of this study included the detailed phenotyping on both metabolic, as well as microbial level. Moreover, this study investigated a heterogeneous, real world, multi-ethnic population which increases the generalizability of the findings. The results of this study add to the ongoing debate whether animal protein intake can be associated with negative metabolic outcomes. 11 This is of importance as this is one of the first studies investigating the effect of protein modulation in an isocaloric fashion taking also the effect on gut microbiota composition and gut-derived metabolites into account. Lastly, this study was not confounded by weight changes, as the majority of dietary intervention studies are which allows a proper interpretation of macronutrient modulation.       iScience Article were followed-up for their T2D. Inclusion criteria were: presence of T2D as defined by the American Diabetes Association, 60 stable use of metformin for R3 months, as metformin therapy is among the first lines of antidiabetic drugs and has a profound effect on gut microbiota, 61,62 BMIR25 kg/m 2 , age 40-70 years, Caucasian, Caribbean or African origin. Exclusion criteria were: use of insulin therapy, antibiotic usage within the last three months, uncontrolled diabetes (HbA1c>9% i.e., 75 mmol/mol), vegetarian diet, presence of chronic inflammatory disease, use of pre-pro-synbiotics, use of proton-pump-inhibitor, eGFR < 50 mL/min/1.73 m 2 , active malignancy, unmotivated or unable to adhere to the diet. The baseline characteristics of participants are described in Table 1.

DECLARATION OF INTERESTS
The study was approved by the local Institutional Review Board of both centers and was carried out simultaneously in Amsterdam UMC, location AMC and Paris, University Hospital Pitié -Salpê triè re, Sorbonne University. The study was conducted in accordance with the Declaration of Helsinki and registered at the clinical trial registry: NCT03732690 and NCT03732690. Written informed consent was obtained from all participants.

Study design
This study was a 12-week randomized controlled, non-blinded, isocaloric dietary intervention clinical trial ( Figure 1). Participants were randomized to follow either a HP diet (HP) or a LP diet (LP) for a duration of 12 weeks and visited the study location fasted at three times (Week 0, Week 6 and Week 12). Before each visit, participants collected 24 h and fresh fecal samples and delivered them in a cool box. Feces samples were immediately frozen in À80 C.
Two weeks before each study visit, participants wore a continuous glucose monitor (Freestyle Libre) in order to obtain real-life data. At baseline and week 12, participants underwent a MMT to determine insulin resistance, which provides a more physiological response compared to oral-glucose tolerance test. 63,64 Primary objective of the study was to investigate the effect of the diet on post-prandial glycemic excursion (AUC) after the MMT. Secondary objectives were to analyze the effects of the diets on (i) post-prandial glycemic excursion using continuous glucose monitors, on glycemic control and metabolic markers such as HbA1c and cholesterol levels, (ii) gut microbiota composition, alpha and beta diversity and (iii) serum gut-derived metabolites levels. All individuals had a moderate to normal renal function, which was defined as an eGFR > 50 mL/min/1.73 m 2 , according to the MDRD formula. 65,66 Diet The objective in the HP group was to reach 30% of total energy intake (En%) from protein. The objective in the LP diet (LP) was to limit protein to 10% of total energy intake (En%). These En% were used as target, as they are among the extreme of the reference intake of protein recommended. 67 In both groups, no caloric restriction was performed. Baseline energy intake requirements were estimated using resting energy expenditure measured by indirect calorimetry adjusted for physical activity level. To avoid the confounding effect of weight changes on the outcomes of this study, participants were instructed to not change their lifestyle throughout this study, with the exception of protein composition in their diet.
Before randomization and during the entire study period, participants had filled out a three-day food diary weekly (two weekdays and one weekend day, randomized every week). Personalized adaptations of their diet were then given by a trained dietician to reach the protein consumption objectives of the allocated group (HP or LP) using a mix of dietary guidance and specific food deliveries to their homes. Participants were provided with HP or LP snacks and meals, which filled approximately half of their total energy intake throughout the study. In the HP group protein supplements consisted mostly out of animal protein, well balanced between red,white meat and fish. The detailed composition of the food supplements is provided in the supplementary information (Table S1) For the other half of their food intake, they were instructed to favor/avoid certain foods using lists of high or LP containing foods and example menus. Throughout the study, participants had weekly phone contact with the dietician to ensure dietary compliance and to provide additional guidance if the protein objectives were not reached. In addition to dietician interviews, compliance to the diet was evaluated using 24-h dietary recalls, collected 3 days per week, so a total of 12*3=36 food diaries per subject, and by collecting 24-h urine samples before each study visit in order to determine the urea/creatinine ratio, a validated marker for protein intake. 68 At baseline and week 12 (end of study), body composition was measured through Bioelectrical impedance analysis and resting energy expenditure through indirect. 69 Blood samples were collected after an overnight fast. Fasting serum glucose, triglycerides and HbA1c were measured using enzymatic methods. Examination of these anthropometric and biological outcomes were part of secondary outcomes to be evaluated.

Mixed-meal test
Participants underwent a 4-h MMT. 63 Briefly, participants visited the study center at baseline and week 12. Subjects were fasted for at least 8 h before the site visit and an intravenous catheter was placed in a distal arm vein. Baseline blood sampling was obtained and afterward participants immediately ingested a liquid meal solution (Nutridrink, Nutricia Advanced Medical Nutrition, Amsterdam, the Netherlands) containing 600 kcal (35% fat, 49% carbohydrates and 16% proteins) blood was sampled at fixed time point for the duration of 4 h, centrifuged and stored in the minus 80 C, until further analysis. Blood was drawn for metabolite analyses at baseline (fasted) and after 240 min post-prandial, after ingestion of the MMT. This procedure was done before the dietary protein intervention and after 12 weeks of following either an HP or an LP diet in order to determine (240 min post prandial) plasma metabolite changes modulated via 12 weeks of dietary protein intervention.
DNA extraction, library preparation and gut microbiota sequencing analysis DNA extraction and library preparation was performed as previously published. 70,71 Briefly, fecal samples were extracted in Lysing Matrix E tubes (MP Biomedicals) containing ASL buffer (Qiagen). Lysis was obtained after homogenization by vortexing for 2 min followed by two cycles of heating at 90 C for 10 min with afterward three bursts of bead beating at 5.5 m s-1 for 60 s in a FastPrep-24 instrument (MP Biomedicals). After each beadbeating burst, samples were placed on ice for 5 min. Supernatants containing fecal DNA were collected after two cycles by centrifugation at 4 C. Supernatants from the two centrifugation steps were pooled, and a 600-mL aliquot from each sample was purified using the QIAamp DNA Mini kit (QIAGEN) in the QIAcube instrument (QIAGEN) using the procedure for human DNA analysis. Samples were eluted in 200 mL of AE buffer (10 mM Tris-Cl, 0.5 mM EDTA, pH 9.0). Libraries for shotgun metagenomic sequencing were prepared by a PCR-free method; library preparation and sequencing were performed at Novogene (Cambridge, UK) on a HiSeq instrument (Illumina) with 150-bp paired-end reads and 6 G data per sample. The MEDUSA pipeline was used to process shotgun metagenomics. 72 Briefly, Total fecal genomic DNA was extracted from 100 to 150 mg of feces by repeated bead beating using a modification of the IHMS DNA extraction protocol Q. 70 libraries for shotgun metagenomic sequencing were prepared by a PCR-free method; library preparation and sequencing were performed at Novogene (Cambridge, UK) on an Illumina NovaSeq 6000 S4 flow cell with 150-bp paired-end reads and 6 G data per sample. Raw reads were processed with a pipeline implemented with NGless v.1.0. 73 Briefly, reads were first preprocessed by filtering basecalls with a phred score below 25 and reads with less than 45 bp of length. Then, contaminants were filtered by mapping the quality filtered reads against a database containing human, animal, fungus and plant genomes (minimum match size = 45 and min identity percentage = 95). Filtered reads were mapped with bwa 74 against the IGC (Integrated Gene Catalog), a 9.9 million human gut microbial genes collection. 75 Gene abundance table was computed with the NGless dist1 option where multiple mapped reads are distributed based on the coverage of singly mapped reads. The gene abundance table generated with NGless was then treated with MetaOminer V1.2 76 for rarefaction to 10 7 reads and RPKM normalization. A second catalog of Co-Abundance Gene groups (CAG) that accompanied the IGC, those CAGs with more than 500 genes were considered as Metagenomic Species (MGS). The relative abundance for each MGS was established as the average abundance of the 50 most correlated genes. Classification at the species level for each MGS was stablished when at least 50% of the MGS matched the same NCBI reference genome at 95% identity and 90% of the length coverage. For superior taxonomic levels, the criteria were relaxed to 85% and 75% of identity to assign genus and phylum respectively. Finally, abundances of gut microbial derived metabolic modules (GMM) were determined from gene abundance tables according the classification proposed by Vieira-Silva. 77 Metabolomics Plasma metabolites were measured using stable-isotope-dilution LC-MS/MS as recently described 39, 78 Quantification of histidine, ImP and urocanate were similarly performed using stable isotope dilution LC-MS/MS, using heavy isotope labeled internal standards (Histidine-D5,15N3, Cambridge Isotope Lab. Inc, ImP-13C3 and urocanate-13C3, Astra Zeneca, Cambridge, UK). 40 ll OPEN ACCESS iScience Article Cambridge Isotope Lab. Inc, ImP-13C3 and urocanate-13C3, Astra Zeneca, Cambridge, UK). After vortexing and centrifugation, the supernatant was transferred to new glass vials and evaporated under a stream of nitrogen. The samples were then reconstituted in 5% HCl (37%) in 1-butanol and placed in oven at 70 C for 1 h allowing the n-butyl ester to be formed. After derivatization, the samples were evaporated and reconstituted in 100 mL of water:acetonitrile [90 :10]. The samples were then analyzed using ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS). The analytical system consisted of an Acquity UPLC I-class binary pump, sample manager and column manager coupled to a Xevo TQ-XS (Waters, Milford, MA, USA). The samples (2 mL) were injected onto a C18 BEH column (2.1 3 50 mm with 1.7 mm particles, Waters, Milford, MA, USA) and separated using a gradient consisting of water with 0.1% formic acid (A-phase) and acetonitrile with 0.1% formic acid (B-phase). The analytes were detected with MRM using the transitions 212/110 (histidine), 197/81 (ImP) and 195/93 (urocanate).
For the internal standards, the transitions 220/118, 200/82 and 198/95 respectively were used. Calibration curves of histidine, ImP and urocanate were made in methanol and treated the same way as the samples.

QUANTIFICATION AND STATISTICAL ANALYSIS
Based on studies and a hypothesized peak-difference in postprandial glucose excursion (13.0 mmol/L in HP group vs. 10.1 LP group; SD 2.8) following the MMT, we calculated that we needed 60 subjects per arm to detect a significant difference in this trial. This number was based on a significance level of 0.05 and 80% power and was calculated using an online power calculation (www.biomath.info/power). Delta changes before and after intervention were calculated and the effect of the intervention (HP vs. LP) on these changes was analyzed using linear mixed effect models generated with lme4 (v1.1.30) and lmerTest (v3.1.3) R packages and linear regression models correcting for baseline value, site center and using subject ID as random effect in the mixed-effect models, to account for baseline differences. For the linear regression model: Delta variable = group +baseline value variable+ center, for the linear mixed effect models delta_change = group+center | ID as random effect. In the metabolic module analysis additional adjustment for ethnicity was added. In addition to determine the significance of the diet in the abundance of the different GMM we compared the fits of the model adjusted for the diet with a simpler model without diet adjustment.
For metabolites related analyses further adjustment was performed on eGFR changes. All statistical analyses were done using the open software program R (version 4.2.1). 79 FreeStyle Libre data were processed using the CGDA package. 80 A p value <0.05 was considered statistically significant. Originally, the aim was to include 120 subject per center in order to have enough power to detect ethnic specific effects in subgroup analysis. However, due to the COVID pandemic, the inclusion rate was hampered and the analysis was restricted to the main objective without sub-group analysis.
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